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Uni Köln
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für Wirtschafts- und Sozialstatistik > Institut
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Mosler > Prof. Mosler > Datenportal
Datenportal des Lehrstuhls für Statistik und Ökonometrie
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Segmentation (C vs W) data
The data set (and description) can be downloaded here:
http://archive.ics.uci.edu/ml/machine-learning-databases/image/
Description:
1. Title: Image Segmentation data
2. Source Information
-- Creators: Vision Group, University of Massachusetts
-- Donor: Vision Group (Carla Brodley, brodley@cs.umass.edu)
-- Date: November, 1990
3. Past Usage: None yet published
4. Relevant Information:
The instances were drawn randomly from a database of 7 outdoor
images. The images were handsegmented to create a classification
for every pixel.
Each instance is a 3x3 region.
5. Number of Instances: Training data: 210 Test data: 2100
6. Number of Attributes: 19 continuous attributes
7. Attribute Information:
1. region-centroid-col: the column of the center pixel of the region.
2. region-centroid-row: the row of the center pixel of the region.
3. region-pixel-count: the number of pixels in a region = 9.
4. short-line-density-5: the results of a line extractoin algorithm that
counts how many lines of length 5 (any orientation) with
low contrast, less than or equal to 5, go through the region.
5. short-line-density-2: same as short-line-density-5 but counts lines
of high contrast, greater than 5.
6. vedge-mean: measure the contrast of horizontally
adjacent pixels in the region. There are 6, the mean and
standard deviation are given. This attribute is used as
a vertical edge detector.
7. vegde-sd: (see 6)
8. hedge-mean: measures the contrast of vertically adjacent
pixels. Used for horizontal line detection.
9. hedge-sd: (see 8).
10. intensity-mean: the average over the region of (R + G + B)/3
11. rawred-mean: the average over the region of the R value.
12. rawblue-mean: the average over the region of the B value.
13. rawgreen-mean: the average over the region of the G value.
14. exred-mean: measure the excess red: (2R - (G + B))
15. exblue-mean: measure the excess blue: (2B - (G + R))
16. exgreen-mean: measure the excess green: (2G - (R + B))
17. value-mean: 3-d nonlinear transformation
of RGB. (Algorithm can be found in Foley and VanDam, Fundamentals
of Interactive Computer Graphics)
18. saturatoin-mean: (see 17)
19. hue-mean: (see 17)
8. Missing Attribute Values: None
9. Class Distribution:
Classes: brickface, sky, foliage, cement, window, path, grass.
30 instances per class for training data.
300 instances per class for test data.
Citation Request:
Please refer to the repository http://archive.ics.uci.edu/ml (see citation policy).
See also Frank, A. & Asuncion, A. (2010). UCI Machine Learning Repository
[http://archive.ics.uci.edu/ml].
Irvine, CA: University of California, School of Information and Computer Science.
Descriptive statistics:
Dataset= segmentation : n= 660 , d= 10
Class1: n= 330
Covariance matrix:
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 4618.9952 223.3175 -0.3150 -0.0296 14.3268 246.3601 45.4397 -240.4204 -1.0481 1.9278
[2,] 223.3175 1428.3118 0.0574 -0.0817 12.3275 40.9520 6.2346 -305.3255 1.1930 -0.1304
[3,] -0.3150 0.0574 0.0021 0.0001 -0.0192 -0.0741 -0.0132 0.0228 0.0000 0.0002
[4,] -0.0296 -0.0817 0.0001 0.0005 0.0010 -0.0047 0.0064 0.0017 0.0000 0.0000
[5,] 14.3268 12.3275 -0.0192 0.0010 12.6602 48.3600 -0.5104 -6.2857 -0.0040 -0.0211
[6,] 246.3601 40.9520 -0.0741 -0.0047 48.3600 911.8912 10.7377 -3.0201 -0.0896 0.0114
[7,] 45.4397 6.2346 -0.0132 0.0064 -0.5104 10.7377 12.3857 -3.3884 -0.0462 0.0558
[8,] -240.4204 -305.3255 0.0228 0.0017 -6.2857 -3.0201 -3.3884 240.9849 -0.6699 -0.1435
[9,] -1.0481 1.1930 0.0000 0.0000 -0.0040 -0.0896 -0.0462 -0.6699 0.0062 -0.0055
[10,] 1.9278 -0.1304 0.0002 0.0000 -0.0211 0.0114 0.0558 -0.1435 -0.0055 0.0136
Correlation matrix:
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 1.0000 0.0869 -0.1019 -0.0192 0.0592 0.1200 0.1900 -0.2279 -0.1952 0.2436
[2,] 0.0869 1.0000 0.0334 -0.0954 0.0917 0.0359 0.0469 -0.5204 0.3996 -0.0296
[3,] -0.1019 0.0334 1.0000 0.1224 -0.1187 -0.0539 -0.0827 0.0323 0.0033 0.0407
[4,] -0.0192 -0.0954 0.1224 1.0000 0.0121 -0.0068 0.0805 0.0047 -0.0094 -0.0138
[5,] 0.0592 0.0917 -0.1187 0.0121 1.0000 0.4501 -0.0408 -0.1138 -0.0142 -0.0509
[6,] 0.1200 0.0359 -0.0539 -0.0068 0.4501 1.0000 0.1010 -0.0064 -0.0375 0.0032
[7,] 0.1900 0.0469 -0.0827 0.0805 -0.0408 0.1010 1.0000 -0.0620 -0.1661 0.1361
[8,] -0.2279 -0.5204 0.0323 0.0047 -0.1138 -0.0064 -0.0620 1.0000 -0.5462 -0.0794
[9,] -0.1952 0.3996 0.0033 -0.0094 -0.0142 -0.0375 -0.1661 -0.5462 1.0000 -0.5932
[10,] 0.2436 -0.0296 0.0407 -0.0138 -0.0509 0.0032 0.1361 -0.0794 -0.5932 1.0000
Median: 135.2506 100.2022 0.0184 0.0022 3.0362 3.2383 2.6272 45.8064 0.2962 -2.0162
Mean: 130.9576 98.3909 0.0182 0.0034 2.9882 5.7268 2.548 44.8697 0.3087 -2.0328
MCD-estimated:
MDC-0.975-Mean: 122.1807 91.4463 0 0 2.1212 1.4752 1.3522 48.7548 0.3095 -2.0631
MDC-0.750-Mean: 122.3408 91.375 0 0 2.0956 1.4755 1.3542 48.7151 0.3095 -2.063
MDC-0.500-Mean: 122.1807 91.4463 0 0 2.1212 1.4752 1.3522 48.7548 0.3095 -2.0631
Class2: n= 330
Covariance matrix:
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 4404.4038 -578.3487 -0.0136 0.1671 3.3112 -25.2207 -11.7488 -29.2875 -3.4030 6.9387
[2,] -578.3487 1172.6146 0.0088 0.0105 -3.5759 27.2982 -2.8230 -106.1587 -0.4449 6.2493
[3,] -0.0136 0.0088 0.0006 0.0000 0.0030 -0.0016 0.0031 0.0388 -0.0001 -0.0010
[4,] 0.1671 0.0105 0.0000 0.0004 0.0140 0.0592 0.0090 0.0122 0.0004 -0.0012
[5,] 3.3112 -3.5759 0.0030 0.0140 3.3612 9.8051 0.7588 4.3410 0.0311 -0.3178
[6,] -25.2207 27.2982 -0.0016 0.0592 9.8051 94.9681 7.0006 6.4833 0.1727 -0.4547
[7,] -11.7488 -2.8230 0.0031 0.0090 0.7588 7.0006 3.5637 3.9886 0.0347 -0.2933
[8,] -29.2875 -106.1587 0.0388 0.0122 4.3410 6.4833 3.9886 81.8782 -0.2237 -2.8163
[9,] -3.4030 -0.4449 -0.0001 0.0004 0.0311 0.1727 0.0347 -0.2237 0.0806 -0.1285
[10,] 6.9387 6.2493 -0.0010 -0.0012 -0.3178 -0.4547 -0.2933 -2.8163 -0.1285 0.4921
Correlation matrix:
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 1.0000 -0.2545 -0.0083 0.1260 0.0272 -0.0390 -0.0938 -0.0488 -0.1806 0.1490
[2,] -0.2545 1.0000 0.0104 0.0154 -0.0570 0.0818 -0.0437 -0.3426 -0.0458 0.2602
[3,] -0.0083 0.0104 1.0000 0.0380 0.0653 -0.0065 0.0669 0.1735 -0.0102 -0.0566
[4,] 0.1260 0.0154 0.0380 1.0000 0.3821 0.3042 0.2381 0.0672 0.0645 -0.0886
[5,] 0.0272 -0.0570 0.0653 0.3821 1.0000 0.5488 0.2192 0.2617 0.0597 -0.2471
[6,] -0.0390 0.0818 -0.0065 0.3042 0.5488 1.0000 0.3805 0.0735 0.0624 -0.0665
[7,] -0.0938 -0.0437 0.0669 0.2381 0.2192 0.3805 1.0000 0.2335 0.0647 -0.2215
[8,] -0.0488 -0.3426 0.1735 0.0672 0.2617 0.0735 0.2335 1.0000 -0.0871 -0.4437
[9,] -0.1806 -0.0458 -0.0102 0.0645 0.0597 0.0624 0.0647 -0.0871 1.0000 -0.6451
[10,] 0.1490 0.2602 -0.0566 -0.0886 -0.2471 -0.0665 -0.2215 -0.4437 -0.6451 1.0000
Median: 171.9031 115.4398 0.0045 0.0037 1.1552 1.4977 1.0339 9.5113 0.4952 -1.7887
Mean: 160.0212 112.5303 0.0051 0.0037 1.1926 2.0731 1.0835 8.8438 0.5102 -1.8096
MCD-estimated:
MDC-0.975-Mean: 155.4624 112.919 0 0 0.5527 0.357 0.4785 8.1871 0.6401 -2.068
MDC-0.750-Mean: 152.3837 109.2034 0 0 0.6731 0.4482 0.5268 8.8607 0.6734 -2.0674
MDC-0.500-Mean: 154.2441 113.6394 0 0 0.5685 0.3695 0.4509 8.3159 0.639 -2.0695
Measures:
Mah.Dist: 3.1768
Mah.Dist-MCD-0.975: 3.771
Mah.Dist-MCD-0.750: 3.6156
Mah.Dist-MCD-0.500: 3.6156
Zuletzt geändert am 17.02.2013
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